Machine Induction of Geospatial Knowledge

@InCollection{Whigham:1992:STRCS,
publisher_address = "Berlin, Germany",
author = "P. A. Whigham and R. I. (Bob) McKay and J. R. Davis",
booktitle = "Theories and Methods of Spatio-Temporal Reasoning in
Geographic Space",
editor = "A. U. Frank and I. Campari and U. Formentini",
isbn13 = "978-3-540-55966-5",
ISSN = "0302-9743",
address = "Pisa, Italy",
month = sep,
notes = "Book Chapter",
pages = "402--417",
publisher = "Springer-Verlag",
series = "Springer Lecture Notes in Computer Science",
title = "Machine Induction of Geospatial Knowledge",
URL = "http://sc.snu.ac.kr/PAPERS/Pisa.pdf",
url1 = "http://www.springer.com/west/home?SGWID=4-102-22-1387865-0&changeHeader=true&referer=www.springeronline.com&SHORTCUT=www.springer.com/3-540-55966-3",
volume = "639",
year = "1992",
keywords = "genetic algorithms, genetic programming",
size = "16 pages",
abstract = "Machine learning techniques such as tree induction
have become accepted tools for developing
generalisations of large data sets, typically for use
with production rule systems in prediction and
classification. The advent of computer based
cartography and the field of geographic information
systems (GIS) has seen a wealth of spatial data
generated and used for decision making and modelling.
We examine the implications of inductive techniques
applied to geospatial data in a logical framework. It
is argued that spatial induction systems will benefit
from the ability to extend their initial representation
language, through feature and relation construction.
The enormous search spaces involved imply a need for
strong biasing techniques to control the generation of
possible representations of the data for all but the
most trivial of cases. A heavily constrained geospatial
domain, topographic representation, is described as one
simplified example of induction across a vector
description of space.",
}